Abstract: An electronic device and a sensitivity adjustment method of a sensor are disclosed. The method includes: in a situation that a target program is executed by the electronic device, receiving sensing data from a target sensor; activating a sensitivity adjustment rule according to the target program; adjusting the sensing data according to the sensitivity adjustment rule; and providing the adjusted sensing data to the target program.

Abstract: The disclosure provides an electronic apparatus, a charging method and a non-volatile computer readable recording medium. The electronic apparatus includes a power module and a processor coupled to the power module. The processor is configured to: obtain a charging start time point of the power module; estimate a charging recovery time point according to usage state information; when an electric quantity of the power module is greater than or equal to a first electric quantity, stop the power module from being charged continuously; and at the charging recovery time point, enable the power module to be charged to a second electric quantity, where the second electric quantity is greater than the first electric quantity.

Abstract: The disclosure provides an electronic apparatus, a charging method and a non-volatile computer readable recording medium. The electronic apparatus includes a power module and a processor coupled to the power module. The processor is configured to: obtain a charging start time point of the power module; estimate a charging recovery time point according to usage state information; when an electric quantity of the power module is greater than or equal to a first electric quantity, stop the power module from being charged continuously; and at the charging recovery time point, enable the power module to be charged to a second electric quantity, where the second electric quantity is greater than the first electric quantity.

Abstract: A risk evaluation method and a risk evaluation device for evaluating an anonymous dataset generated according to an original dataset are provided. The risk evaluation method comprises the following steps. Acquiring a plurality of appearing times respectively corresponding to a plurality of original values of the original dataset. Generating a partition set and a weight table according to a sample parameter, an anonymous parameter and the appearing times. Dividing the original dataset into a plurality of data partitions according to the partition set, and generating a penetration dataset according to the weight table and the data partitions, wherein the penetration dataset comprises a plurality of sample data. Comparing each sample data with a plurality of anonymous data of the anonymous dataset to obtain a plurality of matching quantities respectively corresponding to the sample data. And calculating and outputting a risk evaluation result according to the matching quantities.

Abstract: An anonymous dataset generation method comprises following steps. A critical attribute set and a quasi-identifier (QID) set are acquired, and one of the critical attribute and the quasi-identifier is set as an anchor attribute. An attribute sequence and an equivalence table are generated according to the quasi-identifier set and the critical attribute set. A data cluster and a cluster table are generated according to the equivalence table. The content of the cluster table is generalized to generate and output an anonymous dataset corresponding to an original dataset. A risk evaluation method for an anonymous dataset calculates data weight to extract distinctive data and to attacking defects of the anonymous dataset according to the distinctive data, thereby enhancing a risk evaluation efficiency of the anonymous dataset.

Abstract: A risk evaluation method and a risk evaluation device for evaluating an anonymous dataset generated according to an original dataset are provided. The risk evaluation method comprises the following steps. Acquiring a plurality of appearing times respectively corresponding to a plurality of original values of the original dataset. Generating a partition set and a weight table according to a sample parameter, an anonymous parameter and the appearing times. Dividing the original dataset into a plurality of data partitions according to the partition set, and generating a penetration dataset according to the weight table and the data partitions, wherein the penetration dataset comprises a plurality of sample data. Comparing each sample data with a plurality of anonymous data of the anonymous dataset to obtain a plurality of matching quantities respectively corresponding to the sample data. And calculating and outputting a risk evaluation result according to the matching quantities.

Abstract: An anonymous dataset generation method comprises following steps. A critical attribute set and a quasi-identifier (QID) set are acquired, and one of the critical attribute and the quasi-identifier is set as an anchor attribute. An attribute sequence and an equivalence table are generated according to the quasi-identifier set and the critical attribute set. A data cluster and a cluster table are generated according to the equivalence table. The content of the cluster table is generalized to generate and output an anonymous dataset corresponding to an original dataset. A risk evaluation method for an anonymous dataset calculates data weight to extract distinctive data and to attacking defects of the anonymous dataset according to the distinctive data, thereby enhancing a risk evaluation efficiency of the anonymous dataset.